Intercomparison of wind observations from ESA's satellite mission Aeolus, ERA5 reanalysis and radiosonde over China

Abstract. The European Space Agency (ESA) Earth Explorer Atmospheric Dynamics Mission Aeolus is the first satellite mission providing wind profile information on a global scale, and its wind products have been released on 12 May 2020. In this study, we verify and intercompare the wind observations from ESA’s satellite mission Aeolus and the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation atmospheric reanalyses (ERA5) with radiosonde (RS) observations over China, to allow a fitting application of Aeolus winds. Aeolus provides wind observations in aerosol-free (referred to as Rayleigh-clear winds) and cloudy atmospheres (Mie-cloudy winds). In terms of Aeolus and RS winds, the correlation coefficient (R) and mean difference of Rayleigh-clear (Mie-cloudy) vs RS winds are 0.90 (0.92) and 0.09±9.62 (−0.59±8.05) m/s, respectively. The vertical profiles of wind speed differences between Aeolus and RS winds are opposite to each other during ascending and descending orbits, indicating that the performance of Aeolus wind product is affected by the orbit phase. The comparison of ECMWF winds relative to Aeolus winds provides the R and mean difference of Rayleigh-clear (Mie-cloudy) winds, which are 0.95 (0.97) and −0.16±6.78 (−0.21±3.91) m/s, respectively. The Rayleigh-clear and Mie-cloudy winds are almost consistent with the ECMWF winds, likely due to the assimilation of Aeolus wind observations into the ECMWF winds. Moreover, we find that among the results of comparing Aeolus with RS and ECMWF winds, the wind speed difference of Rayleigh-clear winds is large in the height range of 0–1 km, especially during descending orbits. This indicates that the performance of low-altitude Rayleigh-clear wind products could be affected by the near-surface aerosols. In addition, the R and mean difference between ERA5 and RS zonal wind components are 0.89 and −1.46±6.33 m/s, respectively. The RS zonal winds tend to be larger than those from ERA5. The wind speed difference between RS and ERA5 zonal winds in low-lying area is low and insignificant, while it is relatively high and significant over the Qinghai-Tibet Plateau areas. Overall, the Aeolus winds over China are similar to the RS and ECMWF winds. The RS and ERA5 zonal winds are somewhat different over high altitude area, but these differences are acceptable for application of wind products. The findings give us sufficient confidence and information to apply Aeolus wind products in numerical weather prediction in China and in climate change research.

Download Full-text

Wind plants can impact long-term local atmospheric conditions

Scientific Reports ◽

10.1038/s41598-021-02089-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nicola Bodini ◽

Julie K. Lundquist ◽

Patrick Moriarty

Keyword(s):

Wind Speed ◽

Urban Areas ◽

Weather Prediction ◽

Atmospheric Conditions ◽

Near Surface ◽

Direct Observations ◽

Stable Conditions ◽

Weather And Climate ◽

Central United States

AbstractLong-term weather and climate observatories can be affected by the changing environments in their vicinity, such as the growth of urban areas or changing vegetation. Wind plants can also impact local atmospheric conditions through their wakes, characterized by reduced wind speed and increased turbulence. We explore the extent to which the wind plants near an atmospheric measurement site in the central United States have affected their long-term measurements. Both direct observations and mesoscale numerical weather prediction simulations demonstrate how the wind plants induce a wind deficit aloft, especially in stable conditions, and a wind speed acceleration near the surface, which extend $$\sim 30$$ ∼ 30 km downwind of the wind plant. Turbulence kinetic energy is significantly enhanced within the wind plant wake in stable conditions, with near-surface observations seeing an increase of more than 30% a few kilometers downwind of the plants.

Download Full-text

Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

Monthly Weather Review ◽

10.1175/mwr-d-11-00352.1 ◽

2012 ◽

Vol 140 (9) ◽

pp. 3017-3038 ◽

Cited By ~ 138

Author(s):

Anna C. Fitch ◽

Joseph B. Olson ◽

Julie K. Lundquist ◽

Jimy Dudhia ◽

Alok K. Gupta ◽

...

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Kinetic Energy ◽

Wind Farm ◽

Weather Prediction ◽

Wind Farms ◽

Mean Flow ◽

Thrust Coefficient ◽

Near Surface ◽

Neutral Boundary Layer

Abstract A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

Download Full-text

Parameterizations of Sea-Spray Impact on the Air–Sea Momentum and Heat Fluxes

Monthly Weather Review ◽

10.1175/mwr-d-11-00007.1 ◽

2011 ◽

Vol 139 (12) ◽

pp. 3781-3797 ◽

Cited By ~ 54

Author(s):

J.-W. Bao ◽

C. W. Fairall ◽

S. A. Michelson ◽

L. Bianco

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

Weather Prediction ◽

Heat Fluxes ◽

Sea Spray ◽

Surface Boundary ◽

Near Surface ◽

The Mean ◽

Momentum And Heat Fluxes ◽

The Impact

Abstract This paper focuses on parameterizing the effect of sea spray at hurricane-strength winds on the momentum and heat fluxes in weather prediction models using the Monin–Obukhov similarity theory (a common framework for the parameterizations of air–sea fluxes). In this scheme, the mass-density effect of sea spray is considered as an additional modification to the stratification of the near-surface profiles of wind, temperature, and moisture in the marine surface boundary layer (MSBL). The overall impact of sea-spray droplets on the mean profiles of wind, temperature, and moisture depends on the wind speed at the level of sea-spray generation. As the wind speed increases, the mean droplet size and the mass flux of sea-spray increase, rendering an increase of stability in the MSBL and the leveling-off of the surface drag. Sea spray also tends to increase the total air–sea sensible and latent heat fluxes at high winds. Results from sensitivity testing of the scheme in a numerical weather prediction model for an idealized case of hurricane intensification are presented along with a dynamical interpretation of the impact of the parameterized sea-spray physics on the structure of the hurricane boundary layer.

Download Full-text

Impact of assimilation of radiosonde and UAV observations on numerical weather prediction analyses and forecasts in the Arctic and Antarctic

10.5194/egusphere-egu2020-21750 ◽

2020 ◽

Author(s):

Timo Vihma ◽

Tuomas Naakka ◽

Qizhen Sun ◽

Tiina Nygård ◽

Michael Tjernström ◽

...

Keyword(s):

Wind Speed ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

The Arctic ◽

Observation Site ◽

Near Surface ◽

Numerical Weather ◽

Background Fields ◽

The Impact

Weather forecasting in the Arctic and Antarctic is a challenge above all due to rarity of observations to be assimilated in numerical weather prediction (NWP) models. As observations are expensive and logistically challenging, it is important to evaluate the benefit that additional observations could bring to NWP.Considering the Arctic, in this study the effects of the spatial coverage of the network on numerical weather prediction were evaluated by comparing radiosonde observations from land station taken from Integrated Global Radiosonde Archive (IGRA) and radiosonde observations from expeditions in the Arctic Ocean with operational analyses and background fields (12&#8208;hr forecasts) of the European Centre for Medium Range Weather Forecasts (ECMWF). The focus was on 850 hPa level temperature for the period January 2016 &#8211; September 2018. Comparison of the analyses and background fields showed that radiosoundings had a remarkable impact on improving operational analyses but the impact had a large geographical variation. In particular, radiosonde observations from islands (Jan Mayen and Bear Island) in the northern North Atlantic and from Arctic expeditions substantially improved analyses suggesting that those observations were critical for the quality of analyses and forecasts. Comparison of two cases with and without assimilation of radiosonde sounding data from expeditions of Icebreaker Oden in 2016 and 2018 in the central Artic Ocean showed that satellite observations were not able to compensate for the large spatial gap in the radiosounding network. In the areas where the network is reasonably dense, the density of the sounding network was not the most critical factor for the quality of background fields. Instead, the quality of background field was more related to how radiosonde observations were utilized in the assimilation and to the quality of those observations.Considering the Antarctic, we applied radiosonde sounding and Unmanned Aerial Vehicles (UAV) observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting (Polar WRF) model. Our experiments revealed small or moderate impacts of radiosonde and UAV data assimilation. In any case, the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature, wind speed, and humidity at the observation site for most of the time. Further, the impact on the results of 5-day long Polar WRF experiments was often felt over distances of at least 300 km from the observation site. All experiments succeeded in capturing the main features of the evolution of near-surface variables, but the effects of data assimilation varied between different cases. Due to the limited vertical extent of the UAV observations, the impact of their assimilation was limited to the lowermost 1-2 km layer, and assimilation of radiosonde data was more beneficial for modelled sea level pressure and near-surface wind speed. Considering the perspectives for technological advance, atmospheric soundings applying UAV have a large potential to supplement conventional radiosonde sounding observations.The differences in the results obtained for the Arctic and Antarctic are discussed.

Download Full-text

Impacts of Air–Sea Flux Parameterizations on the Intensity and Structure of Tropical Cyclones

Monthly Weather Review ◽

10.1175/mwr-d-12-00274.1 ◽

2013 ◽

Vol 141 (7) ◽

pp. 2308-2324 ◽

Cited By ~ 65

Author(s):

Benjamin W. Green ◽

Fuqing Zhang

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Tropical Cyclones ◽

Latent Heat ◽

Weather Prediction ◽

Surface Flux ◽

Surface Fluxes ◽

Sensible Heat ◽

Near Surface ◽

Flux Parameterization

Abstract Fluxes of momentum and moist enthalpy across the air–sea interface are believed to be one of the most important factors in determining tropical cyclone intensity. Because these surface fluxes cannot be directly resolved by numerical weather prediction models, their impacts on tropical cyclones must be accounted for through subgrid-scale parameterizations. There are several air–sea surface flux parameterization schemes available in the Weather Research and Forecasting (WRF) Model; these schemes differ from one another in their formulations of the wind speed–dependent exchange coefficients of momentum, sensible heat, and moisture (latent heat). The effects of surface fluxes on the intensity and structure of tropical cyclones are examined through convection-permitting WRF simulations of Hurricane Katrina (2005). It is found that the intensity (and, to a lesser extent, structure) of the simulated storms is sensitive to the choice of surface flux parameterization scheme. In agreement with recent studies, the drag coefficient CD is found to affect the pressure–wind relationship (between minimum sea level pressure and maximum 10-m wind speed) and to change the radius of maximum near-surface winds of the tropical cyclone. Fluxes of sensible and latent heat (i.e., moist enthalpy) affect intensity but do not significantly change the pressure–wind relationship. Additionally, when low-level winds are strong, the contribution of dissipative heating to calculations of sensible heat flux is not negligible. Expanding the sensitivity tests to several dozen cases from the 2008 to 2011 Atlantic hurricane seasons demonstrates the robustness of these findings.

Download Full-text

Boundary Layer and Surface Verification of the High-Resolution Rapid Refresh, Version 3

Weather and Forecasting ◽

10.1175/waf-d-20-0101.1 ◽

2020 ◽

Vol 35 (6) ◽

pp. 2255-2278

Author(s):

Robert G. Fovell ◽

Alex Gallagher

Keyword(s):

Boundary Layer ◽

High Resolution ◽

Wind Speed ◽

Prediction Models ◽

Weather Prediction ◽

Potential Temperature ◽

Ground Level ◽

Systematic Errors ◽

Radiosonde Data ◽

Near Surface

AbstractWhile numerical weather prediction models have made considerable progress regarding forecast skill, less attention has been paid to the planetary boundary layer. This study leverages High-Resolution Rapid Refresh (HRRR) forecasts on native levels, 1-s radiosonde data, and (primarily airport) surface observations across the conterminous United States. We construct temporally and spatially averaged composites of wind speed and potential temperature in the lowest 1 km for selected months to identify systematic errors in both forecasts and observations in this critical layer. We find near-surface temperature and wind speed predictions to be skillful, although wind biases were negatively correlated with observed speed and temperature biases revealed a robust relationship with station elevation. Above ≈250 m above ground level, below which radiosonde wind data were apparently contaminated by processing, biases were small for wind speed and potential temperature at the analysis time (which incorporates sonde data) but became substantial by the 24-h forecast. Wind biases were positive through the layer for both 0000 and 1200 UTC, and morning potential temperature profiles were marked by excessively steep lapse rates that persisted across seasons and (again) exaggerated at higher elevation sites. While the source or cause of these systematic errors are not fully understood, this analysis highlights areas for potential model improvement and the need for a continued and accessible archive of the data that make analyses like this possible.

Download Full-text

Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

Weather and Forecasting ◽

10.1175/waf-d-16-0052.1 ◽

2016 ◽

Vol 31 (6) ◽

pp. 1929-1945 ◽

Cited By ~ 11

Author(s):

Michaël Zamo ◽

Liliane Bel ◽

Olivier Mestre ◽

Joël Stein

Keyword(s):

Wind Speed ◽

Random Forests ◽

Numerical Weather Prediction ◽

Prediction Models ◽

Numerical Models ◽

Weather Prediction ◽

Forecast Errors ◽

Near Surface ◽

Numerical Weather ◽

Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

Download Full-text

Evaluation of Single-Doppler Radar Wind Retrievals in Flat and Complex Terrain

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0297.1 ◽

2014 ◽

Vol 53 (8) ◽

pp. 1920-1931 ◽

Cited By ~ 3

Author(s):

Rob K. Newsom ◽

Larry K. Berg ◽

Mikhail Pekour ◽

Jerome Fast ◽

Qin Xu ◽

...

Keyword(s):

Wind Speed ◽

Complex Terrain ◽

Doppler Radar ◽

Wind Fields ◽

Height Range ◽

Central Florida ◽

Speed Difference ◽

Independent Observations ◽

Standard Deviations ◽

Wind Retrievals

AbstractThe accuracy of winds derived from Next Generation Weather Radar (NEXRAD) level-II data is assessed by comparison with independent observations from 915-MHz radar wind profilers. The evaluation is carried out at two locations with very different terrain characteristics. One site is located in an area of complex terrain within the State Line Wind Energy Center in northeastern Oregon. The other site is located in an area of flat terrain on the east-central Florida coast. The National Severe Storm Laboratory’s two-dimensional variational data assimilation (2DVar) algorithm is used to retrieve wind fields from the KPDT (Pendleton, Oregon) and KMLB (Melbourne, Florida) NEXRAD radars. Wind speed correlations at most observation height levels fell in the range from 0.7 to 0.8, indicating that the retrieved winds followed temporal fluctuations in the profiler-observed winds reasonably well. The retrieved winds, however, consistently exhibited slow biases in the range of 1–2 m s−1. Wind speed difference distributions were broad, with standard deviations in the range from 3 to 4 m s−1. Results from the Florida site showed little change in the wind speed correlations and difference standard deviations with altitude between about 300 and 1400 m AGL. Over this same height range, results from the Oregon site showed a monotonic increase in the wind speed correlation and a monotonic decrease in the wind speed difference standard deviation with increasing altitude. The poorest overall agreement occurred at the lowest observable level (~300 m AGL) at the Oregon site, where the effects of the complex terrain were greatest.

Download Full-text

Sensitivity of Tropical Cyclone Simulations to Parametric Uncertainties in Air–Sea Fluxes and Implications for Parameter Estimation

Monthly Weather Review ◽

10.1175/mwr-d-13-00208.1 ◽

2014 ◽

Vol 142 (6) ◽

pp. 2290-2308 ◽

Cited By ~ 21

Author(s):

Benjamin W. Green ◽

Fuqing Zhang

Keyword(s):

Wind Speed ◽

Hurricane Katrina ◽

Prediction Models ◽

Weather Prediction ◽

The Other ◽

High Wind ◽

Surface Exchange ◽

Near Surface ◽

Wind Speeds ◽

Two Parameters

Abstract Tropical cyclones (TCs) are strongly influenced by fluxes of momentum and moist enthalpy across the air–sea interface. These fluxes cannot be resolved explicitly by current-generation numerical weather prediction models, and therefore must be accounted for via empirical parameterizations of surface exchange coefficients (CD for momentum and Ck for moist enthalpy). The resultant model uncertainty is examined through hundreds of convection-permitting Weather Research and Forecasting Model (WRF) simulations of Hurricane Katrina (2005) by varying four key parameters found in commonly used parameterizations of the exchange coefficient formulas. Two of these parameters effectively act as multiplicative factors for the exchange coefficients over all wind speeds (one each for CD and Ck); the other two parameters control the behavior of CD at very high wind speeds (i.e., above 33 m s−1). It is found that both the intensity and the structure of TCs are highly dependent upon the two multiplicative parameters. The multiplicative parameter for CD has a considerably larger impact than the one for Ck on the relationship between maximum 10-m wind speed and minimum sea level pressure: CD alters TC structure, with higher values shifting the radius of maximum winds inward and strengthening the low-level inflow; Ck only affects structure by uniformly strengthening/weakening the primary and secondary circulations. The TC exhibits the greatest sensitivities to the two multiplicative parameters after a few hours of model integration, suggesting that these parameters could be estimated by assimilating near-surface observations. The other two parameters are likely more difficult to estimate because the TC is only marginally sensitive to them in small areas of high wind speed.

Download Full-text

A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model

Atmosphere ◽

10.3390/atmos9080291 ◽

2018 ◽

Vol 9 (8) ◽

pp. 291 ◽

Cited By ~ 6

Author(s):

Jinmyeong Jeong ◽

Seung-Jae Lee

Keyword(s):

Wind Speed ◽

Linear Model ◽

Generalized Linear Model ◽

Pearson Correlation ◽

Weather Prediction ◽

Statistical Parameter ◽

Test Site ◽

Near Surface ◽

Correction Technique ◽

Parameter Correction

A statistical post-processing method was developed to increase the accuracy of numerical weather prediction (NWP) and simulation by matching the daily distribution of predicted temperatures and wind speeds using the generalized linear model (GLM) and parameter correction, considering an increase in model bias when the range of the prediction time lengthens. The Land Atmosphere Modeling Package Weather Research and Forecasting model, which provides 12-day agrometeorological predictions for East Asia, was employed from May 2017 to April 2018. Training periods occurred one month prior to and after the test period (12 days). A probabilistic consideration accounts for the relatively short training period. Based on the total and monthly root mean square error values for each test site, the results show an improvement in the NWP accuracy after bias correction. The spatial distributions in July and January were compared in detail. It was also shown that the physical consistency between temperature and wind speed was retained in the correction procedure, and that the GLM exhibited better performance than the quantile matching method based on monthly Pearson correlation comparison. The characteristics of coastal and mountainous sites are different from inland automatic weather stations, indicating that supplements to cover these distinctive topographic locations are necessary.

Download Full-text